Deep Instinct is looking for a Deep Learning & Data Science Team Leader.
The demand for Deep Instinct game-changing solutions is growing, and so are we. We are looking for an enthusiastic, performance-driven, and analytic team player to lead our Deep Learning and Data Science team. The successful candidate will lead the team responsible for Deep Instinct’s DL models and algorithms which are the foundation of our novel prevention capabilities. The role involves close work with fellow threat-research teams, development teams and engineers. The position requires innovation and independence in a fast-paced environment where deliverables are of the highest quality and accuracy.
As the team leader your responsibilities will include partaking, overseeing and managing the teams activities and deliverables which include:
- Keeping up to date with the latest developments in neural networks research and their potential applications to cyber-security.
- Conducting solution-driven research into the unique challenges faced when applying AI in the cyber-security domain.
- Working with cyber-security specialists, data engineers, and security researchers, to understand the specificities of the data and fit solutions to it.
- Developing DL/ML models for solving various problems related to cyber-security.
- Preparing and delivering oral presentations, as well as writing articles in scientific journals.
- Experience in deep learning (natural language processing, computer vision…)
- Experience in working with Python and data science-related libraries (NumPy, Pandas…) as well as deep learning frameworks such as PyTorch, TensorFlow or other related frameworks
- Industry experience in conducting end to end research projects from data analysis and hypothesis formulation to model evaluation and serving for production environments.
- Experience in leading research activity and research teams.
- Ph.D. or MSc degree with four years of industry experience in computer science, mathematics, statistics or a related field.
- Experience in working with unstructured data, binary data and large-scale datasets.
- Knowledge and Experience with adversarial machine learning methods.
- Experience in working with AWS and distributed GPU training.
- Track record of publications in AI/ML/DL conferences.